Supply chain planning, which comprises the logistical plan of an in-house supply chain, is essential to the success of many of today's manufacturing firms. Most manufacturing firms rely on supply chain planning in some form to ensure the timely delivery of products in response to customer demands. Typically, supply chain planning is hierarchical in nature, extending from distribution and production planning driven by customer orders, to materials and capacity requirements planning, to shop floor scheduling, manufacturing execution, and deployment of products. Supply chain planning ensures the smooth functioning of different aspects of production, from the ready supply of components to meet production demands to the timely transportation of finished goods from the factory to the customer.
The modern supply chain often encompasses a vast array of data. The planning applications that create and dynamically revise plans in the supply chain in response to changing demands and capacity require rapid access to data concerning the flow of materials through the supply chain. The efficient operation of the supply chain depends upon the ability of the various plans to adjust to changes, and the way in which the required data is stored determines the ease with which it can be accessed.
In the conventional relational model, supply chain data is stored in multiple relational database tables. If a parameter of a manufacturing order is changed, all of the aspects of the supply chain affected by such change must be re-calculated using the relational tables. Before a planning algorithm can change the date and/or quantity of a manufacturing order in response to changing capacities, for example, it must take into account the effect that the date and/or quantity change will have on other production and sales orders. Such a calculation is very complex, and requires that the algorithm have access to data concerning all the other orders, materials and resources that would be affected by the change. That information is not readily accessible in the conventional model, and instead must be calculated by tracing through relational database tables. Such calculations are cumbersome and delay planning functions unnecessarily.
There is therefore a need for all data relevant to supply chain planning to be stored in an efficient manner which reflects the progress of materials and orders along the supply chain. There is also a need for such data to be made available to planning algorithms in the most efficient and usable manner possible so as to reduce drastically the runtime of the planning functions.